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Flax Growth Modelling

Logistic model reveals early sowing as key to maximizing flax (Linum usitatissimum L.) vegetative growth under subtropical conditions

🌱 Logistic Model Reveals Early Sowing as Key to Maximizing Flax Vegetative Growth

A comprehensive nonlinear growth analysis of Linum usitatissimum L. under subtropical conditions using logistic models, degree-day accumulation, and derivative-based critical point estimation.

📍 UFSC – Brazil 🔬 Agronomy Journal 📦 R · ggplot2 · nls 📅 2025

About the study

Flax (Linum usitatissimum L.) is a dual-purpose crop of increasing interest in subtropical regions. Understanding the vegetative growth dynamics in response to thermal time is essential for optimizing sowing windows and maximizing fibre and seed production.

This study fitted three-parameter logistic models to leaf area, plant height, and leaf number as a function of accumulated growing degree-days (GDD), evaluated across two sowing dates (E1 and E2) and two cultivars (Dourado and Monarca) in Florianópolis, Santa Catarina, Brazil.

Key insights were derived by analysing the first and second derivatives of the fitted curves to identify inflection points (maximum growth rate) and critical acceleration/deceleration thresholds.


Analysis pipeline

🌡️ ### Climate Data Daily temperature and rainfall records processed to compute accumulated GDD for each sowing season.

🍃 ### Leaf Area Logistic model fit to leaf area per plant (cm² plant⁻¹) with ANOVA on estimated parameters.

📏 ### Plant Height Logistic model fit to plant height (cm) and derivative-based identification of maximum elongation rate.

🔢 ### Leaf Number Logistic model fit to cumulative leaf count per plant with critical point analysis.

📊 ### Correlations Pearson correlations among growth variables across measurement dates and treatments.

📐 ### Logistic Model \[y = \frac{\beta_1}{1 + e^{\beta_3(\beta_2 - x)}}\] where x = GDD, β₁ = asymptote, β₂ = inflection point, β₃ = growth rate.


Navigate the analyses

01

Climate Data Processing

02

Leaf Area Model

03

Plant Height Model

04

Leaf Number Model

05

Correlation Analysis

06

Supplementary Tables


Citation

Authors. (2025). Logistic model reveals early sowing as key to maximizing flax (Linum usitatissimum L.) vegetative growth under subtropical conditions. Agronomy Journal. doi: pending

🚀 Start the analysis 👥 About the team Source code
Source Code
---
title: "Flax Growth Modelling"
subtitle: "Logistic model reveals early sowing as key to maximizing flax (*Linum usitatissimum* L.) vegetative growth under subtropical conditions"
page-layout: custom
toc: false
css: styles/custom.scss
---

```{=html}
<section class="hero-section">
  <h1>🌱 Logistic Model Reveals Early Sowing as Key to Maximizing Flax Vegetative Growth</h1>
  <p class="lead">
    A comprehensive nonlinear growth analysis of <em>Linum usitatissimum</em> L. under
    subtropical conditions using logistic models, degree-day accumulation, and derivative-based
    critical point estimation.
  </p>
  <div class="hero-badges">
    <span class="badge bg-light text-dark">📍 UFSC – Brazil</span>
    <span class="badge bg-light text-dark">🔬 Agronomy Journal</span>
    <span class="badge bg-light text-dark">📦 R · ggplot2 · nls</span>
    <span class="badge bg-light text-dark">📅 2025</span>
  </div>
</section>
```

::: {.container .py-5}

## About the study

Flax (*Linum usitatissimum* L.) is a dual-purpose crop of increasing interest in subtropical regions. Understanding the vegetative growth dynamics in response to thermal time is essential for optimizing sowing windows and maximizing fibre and seed production.

This study fitted **three-parameter logistic models** to leaf area, plant height, and leaf number as a function of **accumulated growing degree-days (GDD)**, evaluated across two sowing dates (*E1* and *E2*) and two cultivars (*Dourado* and *Monarca*) in Florianópolis, Santa Catarina, Brazil.

Key insights were derived by analysing the **first and second derivatives** of the fitted curves to identify inflection points (maximum growth rate) and critical acceleration/deceleration thresholds.

---

## Analysis pipeline

::: {.row .g-3 .mb-4}

::: {.col-md-4}
::: {.info-card}
<span class="card-icon">🌡️</span>
### Climate Data
Daily temperature and rainfall records processed to compute accumulated GDD for each sowing season.
:::
:::

::: {.col-md-4}
::: {.info-card}
<span class="card-icon">🍃</span>
### Leaf Area
Logistic model fit to leaf area per plant (cm² plant⁻¹) with ANOVA on estimated parameters.
:::
:::

::: {.col-md-4}
::: {.info-card}
<span class="card-icon">📏</span>
### Plant Height
Logistic model fit to plant height (cm) and derivative-based identification of maximum elongation rate.
:::
:::

::: {.col-md-4}
::: {.info-card}
<span class="card-icon">🔢</span>
### Leaf Number
Logistic model fit to cumulative leaf count per plant with critical point analysis.
:::
:::

::: {.col-md-4}
::: {.info-card}
<span class="card-icon">📊</span>
### Correlations
Pearson correlations among growth variables across measurement dates and treatments.
:::
:::

::: {.col-md-4}
::: {.info-card}
<span class="card-icon">📐</span>
### Logistic Model
$$y = \frac{\beta_1}{1 + e^{\beta_3(\beta_2 - x)}}$$
where *x* = GDD, β₁ = asymptote, β₂ = inflection point, β₃ = growth rate.
:::
:::

:::

---

## Navigate the analyses

<div class="analysis-grid">

<a href="analysis/01_climate.qmd" class="analysis-card">
  <span class="card-num">01</span>
  <h4 class="card-title">Climate Data Processing</h4>
</a>

<a href="analysis/02_leaf_area.qmd" class="analysis-card">
  <span class="card-num">02</span>
  <h4 class="card-title">Leaf Area Model</h4>
</a>

<a href="analysis/03_plant_height.qmd" class="analysis-card">
  <span class="card-num">03</span>
  <h4 class="card-title">Plant Height Model</h4>
</a>

<a href="analysis/04_leaf_number.qmd" class="analysis-card">
  <span class="card-num">04</span>
  <h4 class="card-title">Leaf Number Model</h4>
</a>

<a href="analysis/05_correlations.qmd" class="analysis-card">
  <span class="card-num">05</span>
  <h4 class="card-title">Correlation Analysis</h4>
</a>

<a href="analysis/06_supplementary.qmd" class="analysis-card">
  <span class="card-num">06</span>
  <h4 class="card-title">Supplementary Tables</h4>
</a>

</div>

---

## Citation

> **Authors.** (2025). Logistic model reveals early sowing as key to maximizing flax (*Linum usitatissimum* L.) vegetative growth under subtropical conditions. *Agronomy Journal*. doi: [pending]()

```{=html}
<div class="d-flex gap-3 mt-3 flex-wrap">
  <a class="btn btn-success" href="analysis/01_climate.qmd">
    🚀 Start the analysis
  </a>
  <a class="btn btn-outline-secondary" href="about.qmd">
    👥 About the team
  </a>
  <a class="btn btn-outline-secondary" href="https://github.com/nepem-ufsc/paper_flax_growth" target="_blank">
    <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" viewBox="0 0 16 16" style="margin-right:4px"><path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0016 8c0-4.42-3.58-8-8-8z"/></svg>
    Source code
  </a>
</div>
```

:::

© 2025 NEPEM – Núcleo de Estudos em Pesquisa e Experimentação e Melhoramento Vegetal | UFSC

 
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